Browsing by Author "Ram Pravesh Kumar"

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Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India
(Academic Press Inc., 2024) Ram Pravesh Kumar; Ranjit Singh; Pradeep Kumar; Ritesh Kumar; Shadman Nahid; Sudhir Kumar Singh; Charanjeet Singh Nijjar
The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27–34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention. © 2024 Elsevier Inc.
Machine learning models for estimating criteria pollutants and health risk-based air quality indices over eastern coast coal mine complex belts
(Frontiers Media SA, 2025) Pradeep Kumar; Arti K. Choudhary; Pawan Kumar Joshi; Ram Pravesh Kumar; R. Bhatla
Estimating criteria pollutants is crucial due to their continuous increase and impact on respiratory health. To mitigate the impact of air pollution on human health, it is essential to understand the concentration of air pollutants at specific locations. This study aims to evaluate the variation, estimate the levels of criteria pollutants, and assess their potential health risks in the vicinity of a coal mine complex and a thermal power plant situated in an eastern coastal state of India. The pre-existing hot spot regions—Talcher (T) and Brajrajnagar (B)—which host many coal-fired power plants and clusters of coal-mining blocks in the coastal state of Odisha, are considered. Talcher consistently shows higher levels of particulate matter (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2), reflecting a greater industrial impact. Brajrajnagar, while also impacted, exhibits comparatively lower pollutant concentrations. The observed seasonal trends highlight the necessity for targeted mitigation strategies to address pollution levels and associated health risks in these regions. Novel machine learning (ML) models, including independent component regression (ICR), ElasticNet (ENET), and boosted tree (BT), are applied to estimate criteria pollutants. Statistical analyses highlight BT as the superior model, outperforming ENET and ICR in pollutant estimation, particularly in Talcher. Taylor plots and statistical evaluations further validate the BT model’s robustness in air pollutant estimation. Additionally, the study assesses the associated health risks posed to nearby populations of Talcher and Brajrajnagar. The analysis highlights significant spatial disparities in pollution levels, with Talcher consistently recording higher concentrations of PM10, NO2, and SO2 and poorer air quality index (AQI) than Brajrajnagar. Talcher also shows greater health risks, with pollutant exposure linked up to 6% higher risks for PM10, 5% for NO2, and up to 3% for SO2. The health risk-based air quality index (HAQI) reveals an underestimation of health risks by the current AQI, emphasizing the need for improved metrics to address the impacts of multi-pollutant exposure. © © 2025 Kumar, Choudhary, Joshi, Kumar and Bhatla.
Spatio-temporal analysis of air pollution and meteorological influences in western Uttar Pradesh using Geospatial techniques: insights for policy and management
(Taylor and Francis Ltd., 2025) Ram Pravesh Kumar; Aafreen Jahan; Ranjit Singh; Pradeep Kumar; Rajesh Bag; R. Bhatla; Balram Ambade; Umesh Chandra Dumka
Recently, air pollution has emerged as a critical environmental challenge, posing significant risks to human health and ecosystems. This study presents a comprehensive spatiotemporal assessment of six major air pollutants (PM₂.₅, NO₂, SO₂, O₃, CO, NH₃) across seven cities of Western Uttar Pradesh (WUP), India (2019–2022), using Geospatial techniques. The findings reveal significant seasonal and spatial variability driven by anthropogenic emissions and meteorological factors. PM₂.₅ levels peaked during winter, ranging from 140 to 181 µg m−3 in Ghaziabad and NOIDA, exceeding the CPCB annual standard by over 9–13 times. NO₂ concentrations also peaked in winter, surpassing 80 µg m−3 in industrial areas, while SO₂ exhibited summer maxima exceeding 25 µg m−3 in Bulandshahr and Agra. O₃ levels were highest during summer and post-monsoon, increasing from 38.03 µg m−3 to 51.20 µg m−3 in Muzaffarnagar over the study period. CO concentrations remained high in winter, reaching 1.54 mg m−3 in NOIDA, and NH₃ showed post-monsoon peaks exceeding 35 µg m−3 in agricultural regions. Correlation analysis showed strong associations between PM₂.₅ and NO₂ (r = 0.80), and NH₃ (r = 0.67), indicating dominant emission sources from vehicular, industrial, and agricultural activities. Random forest regression identified temperature Relative Importance Scores (RIS 0.258) and relative humidity (RIS = 0.242) as key predictors for PM₂.₅, with the model explaining 69.1% of its variability (R2 = 0.691). Air Quality Index (AQI) analysis revealed that Ghaziabad and Baghpat experienced 60.64% and 40.86% of days in the ‘Severe’ category, respectively, highlighting critical air quality deterioration. These findings emphasize the urgent need for season-specific and location-sensitive air pollution mitigation strategies that integrate emission control and meteorological influences to improve public health and environmental sustainability in WUP, aligning with Sustainable Development Goals 3 and 11. © 2025 Informa UK Limited, trading as Taylor & Francis Group.